Comparative study of gastric cancer and chronic gastritis via network analysis.

Aim
In this study the significant differentially expressed genes (DEGs) related to gastric cancer (GC) and chronic gastritis were screened to introduce common and distinctive genes between the two diseases.


Background
Diagnosis of gastric cancer as a mortal disease and chronic gastritis the stomach disorder which can be considered as risk factor of GCs required safe and effective molecular biomarkers.


Methods
Microarray profiles were downloaded from Gene Expression Omnibus (GEO) and analyzed via GEO2R. The candidate DEGs plus relevant genes from STRING database were interacted by Cytoscape software version 3.6.0 the central nodes were determined and analyzed.


Results
JUN, GAPDH, FOS, TP53, PRDM10, VEGFA, and CREB1 as central nodes and TFF1 and ERG1 as the top changed expressed genes were determined as critical nodes related to gastric cancer. GAPDH, PRDM10, TP53, JUN, AKT1, EGFR, MAPK1, EGF, DECR1, and MYC were identified as common remarkable genes between GC and chronic gastritis.


Conclusion
Identification of distinctive and common genes between GC and chronic gastritis can be useful in the early stage detection of disease and reducing risk of GCs.


Introduction
1 Gastric cancer (GC) is the third leading cause of cancer mortality in the world, especially in East Asia (1). Since GC biomarkers are not sufficiently sensitive and specific for diagnostic proposes endoscopy as an aggressive method is the common toll in diagnosis (2). Chronic gastritis the other stomach disorder is characterized by multistep, progressive, and life-long inflammation disease (3). Investigations indicated that there is correlation between gastritis and GCs (4). Early Antrum mucosal protein, Pepsinogen C, IPO-38 antigen are introduced as gastric cancer (6). However more investigations is required for a definitive proof of the introduction of disease biomarkers (11). Deregulation of IL-6 and TGF-β1 in chronic gastritis is investigated and confirmed (12,13). Here microarray profiles of GCs and chronic gastritis patients versus the healthy samples are analyzed by network analysis to determined possible common and differential molecular features between the both diseases.

Methods
The microarray profiles of 10 healthy people versus 26 chronic gastritis and 35 gastric carcinomas (platform GPL2048) were downloaded from Gene Expression Omnibus (GEO). The profiles were analyzed via GEO2R and matched by box plot analysis. The 250 top significant differentially expressed genes (DEGs) were selected for each groups. Among 250 DEGs the genes that were characterized by fold change (FC) above 1.5 and P-value less than 0.05 were selected as significant genes related to chronic gastritis and gastric carcinomas. The top changed expressed genes were displayed as up and down regulated genes. The candidate DEGs plus 100 and 50 relevant genes from STRING database for chronic gastritis and gastric carcinomas respectively included to construct PPI network by Cytoscape software version 3.6.0 (14). The networks were analyzed by network analyzer plugin of Cytoscape. The networks were visualized and layout based on degree value. The top 10% of nodes based on degree value were identified as hub-nodes for the two diseases. In the similar way based on betweenness the bottleneck-nodes were determined for both diseases. The common hub and bottleneck nodes were introduced as hub-bottlenecks relative to the chronic gastritis and gastric carcinomas. The hubs, bottlenecks, and hub-bottlenecks were analyzed as central genes. For better understanding the common central genes between the both diseases were determined. Also the distinctive central nodes between two diseases were identified and discussed.

Results
Gene expression profiles of 10 healthy samples and 26 patients were selected as control and chronic gastritis groups respectively. Box plot representation of gene expression profiles (figure 1) indicates that the two groups are comparable. Among the 250 top changed expression genes 26 significant DEGs were The gene expression profiles of gastric carcinoma group including 35 patients were compared with the profiles of healthy group (see figure 4). Based on box plot analysis the groups are analogous. The numbers of deregulated genes related to carcinoma (146 genes) was about 6 time greater than the DEGs of chronic gastritis.  Therefore 10 top up and down-regulated DEGs related to carcinoma were selected and represented in the figure 5. The PPI network was constructed by 146 DEGs and additional 50 related genes. The network including 31 isolated genes and a main connected component (see figure 6) was analyzed and the central nodes (table 2) were determined. The common hubs, bottlenecks, and hub-bottlenecks between chronic gastritis and gastric carcinoma based were identified and tabulated in the table 3. The differential central nodes between the two diseases also were considered.  Based on gene expression quantity, TFF1 and EGR1

Discussion
can be introduced as the most critical genes related to chronic gastritis. It's well known that protein function     Investigations indicates that CREB1 is involved in cancer cellular proliferation (26). Although FOS & VEGFA are known as vascular epithelial growth factors therefore they are cell growth factors (27).
As we analyzed features of both diseases genetically, the common and differentially biomarker panels were determined for chronic gastritis and gastric adenocarcinoma. Our suggested markers can be used as diagnostic tools or drug target and also distinctive implements for both diseases. It could be concluded that chronic gastritis and gastric adenocarcinoma can be differentiated based on molecular diagnosis. Also the common molecular pathological pathway between two diseases is arguable. Of course, this material requires more field research.